Xinyue Huang
BME PhD Proposal Presentation

Date: 2025-12-10
Time: 10:00 am - 12:00 pm
Location / Meeting Link: MoSE 3201A / https://gatech.zoom.us/j/92806358895?pwd=MpYwBxTDY19bbZ1ssCO613uQDrLWoq.1&from=addon Passcode: 621797

Committee Members:
Stanislav Y. Emelianov, PhD (Advisor); Costas D. Arvanitis, PhD; Scott J. Hollister, PhD; David N. Ku, MD, PhD; Brooks D. Lindsey, PhD


Title: Development and Application of Ultrasound-Based Multimodal Imaging for Noninvasive Multiscale Hemodynamic Assessment

Abstract:
Hemodynamics, from microcirculation to large vessels, is a fundamental indicator of the body’s systemic function. A comprehensive assessment of hemodynamics would provide critical insights into tissue perfusion and systemic circulation. However, there remain significant gaps in quantitative and noninvasive assessment, including the evaluation of microcirculation in deep tissue and the noninvasive measurement of central venous pressure (CVP). The central hypothesis of this project is that integrating ultrasound (US), photoacoustic (PA), and shear-wave elasticity imaging will enable mapping of tissue anatomy, blood distribution, and mechanical properties at clinically relevant depths. Through this integration and the development of dedicated processing algorithms, it will become possible to quantify hemodynamic processes that have not previously been measurable in vivo. This project aims to develop and apply US-based multimodal imaging technologies for quantitative, noninvasive assessment of hemodynamics, from microvascular perfusion to CVP measurement. Aim 1 will develop an integrated PA-strain method to quantify compression-induced blood displacement and reperfusion dynamics for spatial evaluation of deep-tissue microcirculation. Aim 2 will establish a US elasticity-based imaging approach to estimate CVP noninvasively by relating venous pressure to the deformation and stiffness of surrounding tissues. Aim 3 will apply multimodal US and PA imaging to longitudinally assess skin hemodynamics after craniofacial implantation and predict implant exposure through quantitative vascular biomarkers.